AI-POWERED FEATURE ENGINEERING FOR TABULAR DATA IN FINANCIAL FRAUD DETECTION

The problem of financial fraud detection remains difficult because of the ever changing nature of fraudulent tactics.In this research work, the authors present an AI powered feature engineering framework for helping in enhancing fraud detection capabilities in financial systems.The combination of denoising autoencoder for financial denoising [6], Hidden Markov models to discover sequential patterns in the data [3][7], and transformer based language model for real time detection in temporal data [5] is proposed as a methodology.These advanced methods allow automatic feature extraction, decreased number of manual efforts, and improvement in model performance.emerging technologies like deep learning autoencoders, HMMs and transformer models to design a scalable and flexible solution of financially motivated patterns so as to address the increasingly immature problem of fraud detection in financial institutions.Graph neural networks and self supervised learning models [10] are candidates for future enhancements in the entire process of anomaly detection to further optimize.